This project involves the development of reconstruction algorithms for multislice CT using helical data acquisition. With the recent introduction of multi-row detectors and the ability to acquire data over 360 degrees in less than 500 ms, x-ray CT is undergoing a new phase of rapid innovation. Compared to single-slice CT scanners, CT scanners with multi-row detectors can cover larger volumes, achieve higher axial resolution, avoid motion artifacts due to respiration, and improve the detectability of low-contrast details. These advantages have been identified with 4-row scanners and will become increasingly significant with greater numbers of detector rows. Eventually, a powerful imaging system with the capability of achieving early and reliable diagnosis of numerous diseases will be available for biomedical research. With more than 4 rows it is known that the cone-beam (CB) divergence of the beams cannot be neglected during reconstruction. However, from a mathematical point-of-view, designing an algorithm that accurately accounts for this divergence poses a challenge. The design of helical CB reconstruction algorithms is not a priority for CT manufacturers due to the numerous technological problems hindering the development of scanners with multi-row detectors. However, the future of multi-slice CT depends on progress that can be made in this field. Highly accurate reconstruction algorithms are needed to realize the full potential and development of multi-row scanners. This research project aims to satisfy that need. The specific aims are (1) to implement, characterize, and compare existing helical CB reconstruction algorithms, using a collection of figures-of-merit, and to disseminate the coded algorithms; (2) to derive, implement, and characterize new helical CB reconstruction algorithms that provide 3D images with isotropic spatial resolution and high local temporal resolution (< 300 ms); and (3) to derive, implement and characterize new helical CB reconstruction algorithms that provide 3D images with isotropic spatial resolution and high detectability of low-contrast details (possibly at the expense of temporal resolution). Indirectly, this project will have significant impacts on all aspects of medical imaging - particularly in oncology, angiography, evaluation of infections and cardiac diseases, trauma, and radiotherapy.